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Virtual reality perimetry compared to standard automated perimetry in adults with glaucoma: A systematic review.
PLoS One
January 2025
Division of Ophthalmology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
Purpose: The purpose of this systematic review was to consolidate and summarize available data comparing virtual reality perimetry (VRP) with standard automated perimetry (SAP) in adults with glaucoma. Understanding the utility and diagnostic performance of emerging VRP technology may expand access to visual field testing but requires evidence-based validation.
Methods: A systematic literature search was conducted in 3 databases (PubMed Central, Embase, and Cochrane Central Register of Controlled Trials) from the date of inception to 10/09/2024.
Explainable Deep Learning for Glaucomatous Visual Field Prediction: Artifact Correction Enhances Transformer Models.
Transl Vis Sci Technol
January 2025
Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand.
Purpose: The purpose of this study was to develop a deep learning approach that restores artifact-laden optical coherence tomography (OCT) scans and predicts functional loss on the 24-2 Humphrey Visual Field (HVF) test.
Methods: This cross-sectional, retrospective study used 1674 visual field (VF)-OCT pairs from 951 eyes for training and 429 pairs from 345 eyes for testing. Peripapillary retinal nerve fiber layer (RNFL) thickness map artifacts were corrected using a generative diffusion model.
Comparison of Kinetic, Automated, Tangent Screen, and Novel Disposable Perimetry for the Evaluation of Dermatochalasis and Blepharoptosis.
Ophthalmic Plast Reconstr Surg
January 2025
Cleveland Clinic, Cole Eye Institute, Cleveland, OH.
Purpose: To compare a novel disposable ptosis visual field device to conventional perimetry devices for the evaluation of dermatochalasis and/or blepharoptosis.
Methods: Forty patients from a single academic center participated in this prospective, observational study. Patients with dermatochalasis (skin resting on the eyelashes) and/or blepharoptosis (marginal reflex distance 1 ≤2 mm) were included.
Glaucoma detection and staging from visual field images using machine learning techniques.
PLoS One
January 2025
School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.
Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.
A Practical and Simplified Method for Structure-Function Analysis in Patients with Glaucoma and Retinal Disease.
Retina
January 2025
Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA.
Purpose: To describe an accessible method of structure-function correlation using optical coherence tomography (OCT) and virtual reality perimetry (VRP) for patients with retinal disease and glaucoma and to compare results with those of conventional Humphrey visual fields (HVF).
Methods: Patients with a diagnosis of glaucoma involving the central visual field or macula-involving retinal disease were recruited. Patients underwent ophthalmic examination followed by OCT imaging, HVF, and VRP testing.
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